You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This commit was created on GitHub.com and signed with GitHub’s verified signature.
The key has expired.
Added
The random_crossbar_init argument to memtorch.bh.Crossbar. If true, this is used to initialize crossbars to random device conductances in between 1/Ron and 1/Roff.
CUDA_device_idx to setup.py to allow users to specify the CUDA device to use when installing MemTorch from source.
Implementations of CUDA accelerated passive crossbar programming routines for the 2021 Data-Driven model.
A BiBTeX entry, which can be used to cite the corresponding OSP paper.
Fixed
In the getting started tutorial, Section 4.1 was a code cell. This has since been converted to a markdown cell.
OOM errors encountered when modeling passive inference routines of crossbars.
Enhanced
Templated quantize bindings and fixed semantic error in memtorch.bh.nonideality.FiniteConductanceStates.
The memory consumption when modeling passive inference routines.
The sparse factorization method used to solve sparse linear matrix systems.
The naive_program routine for crossbar programming. The maximum number of crossbar programming iterations is now configurable.
Updated ReadTheDocs documentation for memtorch.bh.Crossbar.
Updated the version of PyTorch used to build Python wheels from 1.9.0 to 1.10.0.